Current (self-reported) fuel type

The numbers of observations with each current fuel type:

## 
##          Smokeles             Smoky Wood_and_or_Plant 
##                17                87                 8

Primary analysis

Investigate the association with current (self-reported) fuel type in the LEX study participants, adjusting for known confounders and stove ventilation. The reference group for this analysis would be the smoky coal users. This would be a categorical analysis, and the results would be a p-value from the likelihood ratio (LR) test of a confounder-only model to a model including the exposure variables, as well as p-values for the contrast of each category of coal use (smokeless coal or plant/wood) to that of smoky coal. FDR correction should be used separately for each of these sets. The main interest would be in the coal-specific findings and perhaps less so in the results from the LR test.

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\ & + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \beta_7 * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2368       0.6340
## Hannum EAA     0.6304       0.6340
## PhenoAge EAA   0.5142       0.6340
## Skin&Blood EAA 0.4887       0.6340
## GrimAge EAA    0.0279       0.2232
## DNAmTL         0.5250       0.6340
## IEAA           0.3694       0.6340
## EEAA           0.6340       0.6340

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) \\ & + \beta_3 * county + \beta_4 * BMI + \beta_5 * ses + \beta_6 * edu + \beta_7 * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.

The estimations of \(\beta_0\), \(\beta_1\) and \(\beta_2\) with given \(Y\) are shown below. The \(\beta_1\) and \(\beta_2\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the given fuel type, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Smokeles}) + \beta_2 * I(\text{Wood_and_or_Plant}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2800       0.8200
## Hannum EAA     0.4890       0.8819
## PhenoAge EAA   0.8936       0.8936
## Skin&Blood EAA 0.5512       0.8819
## GrimAge EAA    0.1672       0.8200
## DNAmTL         0.8624       0.8936
## IEAA           0.3075       0.8200
## EEAA           0.6635       0.8847

Linear relation

Use a trend test to estimate a linear relation across use categories (1=wood, 2=smokeless coal, 3=smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]

##                               coefficient  std pval pval_BHadj
## AgeAccelerationResidual             -1.03 0.74 0.17       0.17
## AgeAccelerationResidualHannum       -0.70 0.64 0.28       0.37
## AgeAccelPheno                       -0.06 0.65 0.93       0.93
## DNAmAgeSkinBloodClockAdjAge         -0.08 0.53 0.88       0.88
## AgeAccelGrim                        -0.11 0.47 0.81       0.82
## DNAmTLAdjAge                        -0.02 0.03 0.60       0.60
## IEAA                                -0.98 0.67 0.15       0.15
## EEAA                                -0.72 0.81 0.38       0.47

Cumulative lifetime (self-reported) fuel type

The numbers of observations with each cumulative lifetime fuel type:

## 
##   Mix Smoky 
##    82    37

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.3222       0.5415
## Hannum EAA     0.4061       0.5415
## PhenoAge EAA   0.6397       0.7311
## Skin&Blood EAA 0.9331       0.9331
## GrimAge EAA    0.0245       0.1960
## DNAmTL         0.3396       0.5415
## IEAA           0.0940       0.3760
## EEAA           0.2773       0.5415

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Mix}) \\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon \end{aligned} \]
where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.

The estimations of \(\beta_0\) and \(\beta_1\) with given \(Y\) are shown below. The \(\beta_1\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the mix fuel type, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Mix}) + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.3532       0.8414
## Hannum EAA     0.7909       0.8414
## PhenoAge EAA   0.8253       0.8414
## Skin&Blood EAA 0.8414       0.8414
## GrimAge EAA    0.1805       0.7220
## DNAmTL         0.6405       0.8414
## IEAA           0.0759       0.6072
## EEAA           0.6484       0.8414

Linear relation

Use a trend test to estimate a linear relation across use categories (1=mix, 2=Smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]

##                               coefficient  std pval pval_BHadj
## AgeAccelerationResidual             -0.88 0.95 0.36       0.36
## AgeAccelerationResidualHannum        0.21 0.80 0.79       0.79
## AgeAccelPheno                        0.18 0.81 0.83       0.83
## DNAmAgeSkinBloodClockAdjAge          0.14 0.69 0.84       0.84
## AgeAccelGrim                         0.75 0.57 0.19       0.19
## DNAmTLAdjAge                        -0.02 0.04 0.64       0.64
## IEAA                                -1.52 0.86 0.08       0.16
## EEAA                                 0.46 1.02 0.65       0.65

Childhood (self-reported) fuel type

The numbers of observations with each current fuel type:

## 
##      Mix Smokeles    Smoky     Wood 
##       53        5       47       11

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\ & + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \beta_8 * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0412       0.1099
## Hannum EAA     0.1426       0.1901
## PhenoAge EAA   0.2872       0.3282
## Skin&Blood EAA 0.1345       0.1901
## GrimAge EAA    0.0051       0.0408
## DNAmTL         0.4625       0.4625
## IEAA           0.0379       0.1099
## EEAA           0.1276       0.1901

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) \\ & + \beta_4 * county + \beta_5 * BMI + \beta_6 * ses + \beta_7 * edu + \beta_8 * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations and the reference as the smoky fuel type.

The estimations of \(\beta_0\), \(\beta_1\), \(\beta_2\), and \(\beta_3\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), and \(\beta_3\) can be interpreted as “the expected change of Y if switching form the smoky fuel type to the given fuel type, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Limit the analyses in the primary analysis to include only a single observation from each subject (no need for a mixed model). The rationale for this is that it is not so easy to obtain unbiased p-values from a mixed model for FDR testing. This can be remediated during FDR testing but would be good to check.

Full model: \[Y = \beta_0 + \beta_1 * I(\text{Wood}) + \beta_2 * I(\text{Smokeles}) + \beta_3 * I(\text{Mix}) + \epsilon\] Nested model: \[Y = \beta_0 + \epsilon\] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.2833       0.6101
## Hannum EAA     0.3813       0.6101
## PhenoAge EAA   0.8336       0.8336
## Skin&Blood EAA 0.7398       0.8336
## GrimAge EAA    0.0146       0.1168
## DNAmTL         0.5919       0.7892
## IEAA           0.1220       0.4880
## EEAA           0.3340       0.6101

Linear relation

Use a trend test to estimate a linear relation across use categories (1=wood, 2=smokeless coal, 3 = mix coal, 4=smoky coal). Fit the equation: \[Y = \beta_0 + \beta_1 * fuel\_type + \epsilon\]

##                               coefficient  std pval pval_BHadj
## AgeAccelerationResidual             -0.70 0.50 0.16       0.16
## AgeAccelerationResidualHannum       -0.50 0.42 0.24       0.30
## AgeAccelPheno                       -0.13 0.43 0.77       0.93
## DNAmAgeSkinBloodClockAdjAge          0.02 0.36 0.95       0.95
## AgeAccelGrim                         0.27 0.30 0.37       0.37
## DNAmTLAdjAge                         0.01 0.02 0.62       0.99
## IEAA                                -0.87 0.44 0.05       0.06
## EEAA                                -0.52 0.53 0.33       0.39

Clusters based on model-based exposure estimates at or shortly before the visit (clusCUR6)

The file “LEX_clusCUR6.csv” has information on current pollutant exposures, obtained for the 2 years preceding the visit. To reduce multi-collinearity between exposures, exposure prototypes were derived based on hierarchical cluster analysis in combination followed by principal components analysis. These estimates are available for 6 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CUR6_BC_PAH6 – Black carbon (BC) and 6 PAHs
CUR6_PAH31 – a large cluster of 31 PAHs
CUR6_NkF – NkF only
CUR6_PM_RET – Particulate matter (PM) and retene
CUR6_NO2 – NO2 only
CUR6_SO2 – SO2 only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
CUR6_BC_PAH6 0.79 (-0.5, 0.8) -1.32 (-1.4, -0.9) 0.80 (-0.2, 1.1) 0.69 (0.1, 0.7)
(Missing) 3 2 1 0
CUR6_PAH31 0.38 (-0.4, 0.6) -1.14 (-1.4, -0.5) 0.46 (-0.1, 0.6) 0.75 (0.4, 0.8)
(Missing) 3 2 1 0
CUR6_NkF -0.40 (-0.6, 0.7) 0.06 (-0.2, 0.3) -0.51 (-0.6, 0.9) 0.74 (-0.2, 0.7)
(Missing) 3 2 1 0
CUR6_PM_RET -0.32 (-0.5, 0.4) -0.04 (-0.9, 0.3) -0.32 (-0.5, 0.1) 2.49 (0.9, 2.6)
(Missing) 3 2 1 0
CUR6_NO2 0.06 (-0.4, 0.8) 1.00 (0.6, 1.4) -0.06 (-0.5, 0.5) 0.63 (-0.2, 1.3)
(Missing) 3 2 1 0
CUR6_SO2 -0.30 (-0.9, 0.3) 1.37 (0.2, 1.5) -0.30 (-0.9, 0.1) -1.00 (-1.3, -0.9)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1900       0.2533
## Hannum EAA     0.0239       0.0504
## PhenoAge EAA   0.0210       0.0504
## Skin&Blood EAA 0.1401       0.2242
## GrimAge EAA    0.0085       0.0504
## DNAmTL         0.2939       0.3359
## IEAA           0.4320       0.4320
## EEAA           0.0252       0.0504

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1840       0.2968
## Hannum EAA     0.2914       0.3775
## PhenoAge EAA   0.0241       0.0755
## Skin&Blood EAA 0.0283       0.0755
## GrimAge EAA    0.0263       0.0755
## DNAmTL         0.4823       0.4823
## IEAA           0.3303       0.3775
## EEAA           0.1855       0.2968

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4226       0.4830
## Hannum EAA     0.1558       0.2707
## PhenoAge EAA   0.0209       0.1672
## Skin&Blood EAA 0.1692       0.2707
## GrimAge EAA    0.0806       0.2149
## DNAmTL         0.2510       0.3347
## IEAA           0.6041       0.6041
## EEAA           0.0626       0.2149

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0700       0.0800
## Hannum EAA     0.0037       0.0148
## PhenoAge EAA   0.0093       0.0248
## Skin&Blood EAA 0.0426       0.0800
## GrimAge EAA    0.0651       0.0800
## DNAmTL         0.2166       0.2166
## IEAA           0.0509       0.0800
## EEAA           0.0019       0.0148

Clusters based on model-based exposure estimates accrued before age 18 (clusCHLD5)

The file “LEX_clusCHLD5.csv” has information on estimated pollutant exposures during early childhood. Estimates are available for 5 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CHLD5_X7 – a cluster of 7 air pollutants
CHLD5_X33 – a large cluster of 33 air pollutants
CHLD5_NkF – NkF only
CHLD5_NO2 – NO2 only
CHLD5_SO2 – SO2 only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
CHLD5_X7 0.09 (-0.5, 0.5) -0.63 (-0.9, -0.1) 0.10 (-0.5, 0.3) 0.86 (0.7, 1.1)
(Missing) 3 2 1 0
CHLD5_X33 0.23 (-0.7, 1.1) -0.83 (-1.4, 0.1) 0.51 (-0.4, 1.2) 0.95 (-0.1, 1.0)
(Missing) 3 2 1 0
CHLD5_NkF -0.21 (-0.8, 0.7) 0.06 (-0.3, 0.7) -0.45 (-1.0, 0.5) 1.07 (0.5, 1.5)
(Missing) 3 2 1 0
CHLD5_NO2 0.34 (-0.5, 0.8) 0.17 (-0.5, 0.9) 0.43 (-0.6, 0.8) -0.21 (-0.3, 0.2)
(Missing) 3 2 1 0
CHLD5_SO2 0.34 (-0.7, 0.4) 0.45 (0.3, 1.4) 0.34 (-0.9, 0.4) 0.22 (-0.2, 0.3)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4899       0.5599
## Hannum EAA     0.1305       0.2088
## PhenoAge EAA   0.0576       0.1782
## Skin&Blood EAA 0.0716       0.1782
## GrimAge EAA    0.0120       0.0960
## DNAmTL         0.5692       0.5692
## IEAA           0.4260       0.5599
## EEAA           0.0891       0.1782

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.8864       0.8864
## Hannum EAA     0.2901       0.5840
## PhenoAge EAA   0.1416       0.5664
## Skin&Blood EAA 0.3650       0.5840
## GrimAge EAA    0.0208       0.1664
## DNAmTL         0.5466       0.7288
## IEAA           0.6847       0.7825
## EEAA           0.3074       0.5840

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9805       0.9805
## Hannum EAA     0.3840       0.7341
## PhenoAge EAA   0.0700       0.2800
## Skin&Blood EAA 0.1867       0.4979
## GrimAge EAA    0.0634       0.2800
## DNAmTL         0.5506       0.7341
## IEAA           0.7515       0.8589
## EEAA           0.4588       0.7341

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{X7} + \beta_2 * \text{X33} + \beta_3 * \text{NkF} + \beta_4 * \text{NO2} + \beta_5 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.9268       0.9268
## Hannum EAA     0.3979       0.6366
## PhenoAge EAA   0.0655       0.3264
## Skin&Blood EAA 0.2424       0.6366
## GrimAge EAA    0.0816       0.3264
## DNAmTL         0.3621       0.6366
## IEAA           0.7955       0.9091
## EEAA           0.4873       0.6497

Clusters based on model-based lifetime exposure estimates (clusCUM6)

The file “LEX_clus CUM6.csv” has information on estimated cumulative pollutant exposures during the lifecourse. Estimates are available for 6 different prototypes (cluster variables) for a total of 161 subjects and 211 visits. The prototypes are labelled as:

CUM6_BC_NO2_PM – a cluster of BC, NO2, and PM
CUM6_PAH36 – a large cluster of 36 PAHs
CUM6_DlP – DlP only
CUM6_NkF – NkF only
CUM6_RET – retene only
CUM6_SO2 – SO2 only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
CUM6_BC_NO2_PM 0.22 (-0.6, 0.8) 0.19 (-0.3, 0.7) 0.10 (-1.0, 0.8) 1.38 (0.4, 1.6)
(Missing) 3 2 1 0
CUM6_PAH36 0.25 (-0.6, 1.1) -1.00 (-1.2, -0.3) 0.32 (-0.5, 1.2) 0.83 (0.4, 1.4)
(Missing) 3 2 1 0
CUM6_DlP -0.48 (-1.0, 0.8) 0.65 (0.5, 1.1) -0.66 (-1.2, 0.7) 0.42 (0.3, 0.6)
(Missing) 3 2 1 0
CUM6_NkF -0.22 (-0.8, 0.5) -0.07 (-0.3, 0.4) -0.31 (-1.0, 0.4) 1.18 (0.1, 1.7)
(Missing) 3 2 1 0
CUM6_RET -0.22 (-0.7, 0.3) -0.41 (-0.9, 0.3) -0.25 (-0.8, 0.2) 1.71 (1.2, 1.9)
(Missing) 3 2 1 0
CUM6_SO2 0.09 (-0.4, 0.4) 1.13 (0.5, 1.6) -0.03 (-0.9, 0.3) -0.02 (-0.6, 0.1)
(Missing) 3 2 1 0
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4844       0.4844
## Hannum EAA     0.3926       0.4844
## PhenoAge EAA   0.0933       0.3732
## Skin&Blood EAA 0.2405       0.3885
## GrimAge EAA    0.0011       0.0088
## DNAmTL         0.2155       0.3885
## IEAA           0.4703       0.4844
## EEAA           0.2428       0.3885

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.5551       0.7401
## Hannum EAA     0.8488       0.8488
## PhenoAge EAA   0.1559       0.4157
## Skin&Blood EAA 0.2862       0.5202
## GrimAge EAA    0.0170       0.1360
## DNAmTL         0.1043       0.4157
## IEAA           0.3251       0.5202
## EEAA           0.7581       0.8488

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.6381       0.6381
## Hannum EAA     0.5536       0.6342
## PhenoAge EAA   0.0248       0.1984
## Skin&Blood EAA 0.1313       0.2626
## GrimAge EAA    0.1039       0.2626
## DNAmTL         0.0790       0.2626
## IEAA           0.4141       0.6342
## EEAA           0.5549       0.6342

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_NO2_PM} + \beta_2 * \text{PAH36} + \beta_3 * \text{DlP} + \beta_4 * \text{NkF} + \beta_5 * \text{RET} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.6380       0.6380
## Hannum EAA     0.3701       0.4914
## PhenoAge EAA   0.0243       0.1944
## Skin&Blood EAA 0.0878       0.2054
## GrimAge EAA    0.1027       0.2054
## DNAmTL         0.0826       0.2054
## IEAA           0.4300       0.4914
## EEAA           0.3562       0.4914

Clusters based on pollutant measurements (clusMEAS6)

The file “LEX_clusMEAS6.csv” has information on measured pollutant exposures during each visit. Estimates are available for 6 different prototypes (cluster variables) for a total of 54 subjects and 54 visits. The prototypes are labelled as:

MEAS6_BC_ PM_RET – a cluster of BC, PM, and retene
MEAS6_X31 – a large cluster of 31 air pollutants
MEAS6_X5 – a smaller cluster of 5 air pollutants
MEAS6_DlP – DlP only
MEAS6_NkF – NkF only
MEAS6_ NO2_SO2 – NO2, and SO2

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
MEAS6_BC_PM_RET 0.05 (-0.6, 0.5) -0.40 (-1.6, -0.3) 0.07 (-0.5, 0.5) 1.08 (0.5, 2.1)
(Missing) 70 10 57 3
MEAS6_X31 0.19 (-0.6, 0.7) -1.02 (-1.8, -0.8) 0.31 (-0.1, 0.8) 0.35 (-0.5, 0.8)
(Missing) 70 10 57 3
MEAS6_X5 -0.14 (-1.0, 1.0) -1.07 (-1.1, -1.0) 0.46 (-0.8, 1.1) 0.55 (-0.1, 0.9)
(Missing) 70 10 57 3
MEAS6_DlP -0.63 (-0.7, 1.3) 0.35 (-0.6, 1.0) -0.69 (-0.7, 1.2) -0.30 (-0.5, 1.3)
(Missing) 70 10 57 3
MEAS6_NkF -0.50 (-0.6, 1.2) -0.39 (-0.6, 0.6) -0.50 (-0.6, 1.2) -0.50 (-0.7, 0.2)
(Missing) 70 10 57 3
MEAS6_NO2_SO2 -0.08 (-0.9, 0.8) 0.98 (0.5, 1.5) -0.37 (-0.9, 0.8) -0.37 (-1.3, 0.2)
(Missing) 70 10 57 3
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0198       0.0568
## Hannum EAA     0.0494       0.0859
## PhenoAge EAA   0.0958       0.1277
## Skin&Blood EAA 0.0027       0.0216
## GrimAge EAA    0.5043       0.5043
## DNAmTL         0.1552       0.1774
## IEAA           0.0537       0.0859
## EEAA           0.0213       0.0568

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2}\\ & + \beta_7 * county + \beta_8 * BMI + \beta_9 * ses + \beta_{10} * edu + \beta_{11} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), \(\beta_5\), and \(\beta_6\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = & \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1034       0.1654
## Hannum EAA     0.0550       0.1306
## PhenoAge EAA   0.0653       0.1306
## Skin&Blood EAA 0.0353       0.1306
## GrimAge EAA    0.1928       0.2571
## DNAmTL         0.2487       0.2842
## IEAA           0.5142       0.5142
## EEAA           0.0263       0.1306

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{BC_PAH6} + \beta_2 * \text{PAH31} + \beta_3 * \text{NkF} + \beta_4 * \text{PM_RET} + \beta_5 * \text{NO2} + \beta_6 * \text{SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0715       0.1834
## Hannum EAA     0.1314       0.2102
## PhenoAge EAA   0.2403       0.3204
## Skin&Blood EAA 0.0446       0.1834
## GrimAge EAA    0.0917       0.1834
## DNAmTL         0.4322       0.4322
## IEAA           0.3579       0.4090
## EEAA           0.0624       0.1834

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = & \beta_0 + \beta_1 * \text{BC_PM_RET} + \beta_2 * \text{X31} + \beta_3 * \text{X5} + \beta_4 * \text{DlP} + \beta_5 * \text{NkF} + \beta_6 * \text{NO2_SO2} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.1992       0.3187
## Hannum EAA     0.1754       0.3187
## PhenoAge EAA   0.2713       0.3617
## Skin&Blood EAA 0.0873       0.2472
## GrimAge EAA    0.0319       0.2472
## DNAmTL         0.4242       0.4848
## IEAA           0.6646       0.6646
## EEAA           0.0927       0.2472

Clusters based on urinary biomarkers (clusURI5)

The file “LEX_clusURI5.csv” has information on measured urinary biomarkers obtained during each visit. Estimates are available for 5 different prototypes (cluster variables) for a total of 163 subjects and 186 visits. The prototypes are labelled as:

URI5_NAP_1M_2M – a cluster of Naphthalene, 1Methylnaphthalene, and 2Methylnaphthalene
URI5_ACE – Acenaphthene only
URI5_FLU_PHE – Fluoranthene and Phenanthrene_anth
URI5_PYR – Pyrene only
URI5_CHR – Baa_Chrysene only

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
MEAS6_BC_PM_RET 0.05 (-0.6, 0.5) -0.40 (-1.6, -0.3) 0.07 (-0.5, 0.5) 1.08 (0.5, 2.1)
(Missing) 70 10 57 3
MEAS6_X31 0.19 (-0.6, 0.7) -1.02 (-1.8, -0.8) 0.31 (-0.1, 0.8) 0.35 (-0.5, 0.8)
(Missing) 70 10 57 3
MEAS6_X5 -0.14 (-1.0, 1.0) -1.07 (-1.1, -1.0) 0.46 (-0.8, 1.1) 0.55 (-0.1, 0.9)
(Missing) 70 10 57 3
MEAS6_DlP -0.63 (-0.7, 1.3) 0.35 (-0.6, 1.0) -0.69 (-0.7, 1.2) -0.30 (-0.5, 1.3)
(Missing) 70 10 57 3
MEAS6_NkF -0.50 (-0.6, 1.2) -0.39 (-0.6, 0.6) -0.50 (-0.6, 1.2) -0.50 (-0.7, 0.2)
(Missing) 70 10 57 3
MEAS6_NO2_SO2 -0.08 (-0.9, 0.8) 0.98 (0.5, 1.5) -0.37 (-0.9, 0.8) -0.37 (-1.3, 0.2)
(Missing) 70 10 57 3
1 Median (IQR)

Primary analysis

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4408       0.6169
## Hannum EAA     0.4716       0.6169
## PhenoAge EAA   0.0226       0.1808
## Skin&Blood EAA 0.8595       0.8595
## GrimAge EAA    0.0945       0.2520
## DNAmTL         0.0815       0.2520
## IEAA           0.5398       0.6169
## EEAA           0.5041       0.6169

Linear regression

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR}\\ & + \beta_6 * county + \beta_7 * BMI + \beta_8 * ses + \beta_{9} * edu + \beta_{10} * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations.

The estimations of \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) with given \(Y\) are shown below. The \(\beta_1\), \(\beta_2\), \(\beta_3\), \(\beta_4\), and \(\beta_5\) can be interpreted as “the expected change of Y if increase one unit of given exposure prototype, while holding other variables constant”.

Sensitivity analyses

Likelihood ratio (LR) test (single model)

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7166       0.9146
## Hannum EAA     0.8605       0.9146
## PhenoAge EAA   0.0779       0.5412
## Skin&Blood EAA 0.5460       0.9146
## GrimAge EAA    0.2178       0.5808
## DNAmTL         0.1353       0.5412
## IEAA           0.7881       0.9146
## EEAA           0.9146       0.9146

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.5630       0.9535
## Hannum EAA     0.7871       0.9535
## PhenoAge EAA   0.1480       0.9535
## Skin&Blood EAA 0.9124       0.9535
## GrimAge EAA    0.8240       0.9535
## DNAmTL         0.5162       0.9535
## IEAA           0.7267       0.9535
## EEAA           0.9535       0.9535

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[Y = \beta_0 + \beta_1 * \text{NAP_1M_2M} + \beta_2 * \text{ACE} + \beta_3 * \text{FLU_PHE} + \beta_4 * \text{PYR} + \beta_5 * \text{CHR} + \epsilon\]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7266       0.9357
## Hannum EAA     0.8797       0.9357
## PhenoAge EAA   0.1130       0.9040
## Skin&Blood EAA 0.6715       0.9357
## GrimAge EAA    0.8736       0.9357
## DNAmTL         0.6495       0.9357
## IEAA           0.8091       0.9357
## EEAA           0.9357       0.9357

Ambient Exposure

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
bap_air 39.44 (18.9, 74.1) 10.09 (4.5, 20.7) 45.22 (21.9, 76.7) 69.11 (57.0, 131.2)
(Missing) 4 0 3 1
pm25_air 139.32 (100.1, 227.1) 120.16 (102.4, 160.7) 137.48 (98.3, 211.0) 421.89 (252.7, 480.4)
ANY_air 564.51 (305.8, 977.5) 477.86 (187.2, 791.4) 560.77 (306.0, 914.7) 7,030.90 (3,125.6, 10,967.7)
(Missing) 35 7 24 4
BPE_air 46.55 (19.5, 73.4) 12.70 (3.9, 19.7) 48.29 (22.9, 83.4) 66.81 (42.4, 114.8)
(Missing) 4 0 3 1
BaA_air 40.51 (16.7, 88.1) 9.44 (2.9, 23.3) 50.23 (20.7, 106.2) 68.31 (61.8, 163.2)
(Missing) 4 0 3 1
BbF_air 62.69 (32.8, 120.9) 31.76 (13.5, 50.1) 65.78 (34.5, 124.7) 88.69 (78.2, 181.6)
(Missing) 4 0 3 1
BkF_air 13.24 (6.4, 25.9) 3.37 (2.0, 7.6) 15.07 (8.0, 28.6) 27.64 (12.5, 48.0)
(Missing) 4 0 3 1
CHR_air 45.82 (16.4, 86.9) 15.24 (4.9, 31.8) 50.79 (18.1, 86.9) 91.89 (61.3, 134.8)
(Missing) 4 0 3 1
DBA_air 12.49 (4.4, 27.5) 3.92 (1.4, 11.0) 14.25 (6.1, 31.8) 12.67 (7.6, 25.3)
(Missing) 4 0 3 1
FLT_air 17.33 (5.1, 41.6) 4.35 (0.6, 7.2) 19.15 (6.5, 41.8) 104.71 (48.9, 175.2)
(Missing) 4 0 3 1
FLU_air 276.10 (165.2, 546.9) 251.42 (219.0, 298.2) 276.10 (159.0, 544.6) 1,426.05 (632.8, 2,241.9)
(Missing) 35 7 24 4
IPY_air 27.29 (14.0, 47.7) 12.70 (4.3, 16.6) 30.70 (15.3, 48.1) 69.17 (51.1, 118.8)
(Missing) 4 0 3 1
NAP_air 3,170.67 (1,807.5, 5,568.9) 3,217.69 (2,288.3, 4,623.5) 3,142.04 (1,759.1, 5,442.8) 29,828.64 (11,068.1, 49,775.1)
(Missing) 35 7 24 4
PHE_air 396.14 (220.9, 820.9) 363.30 (294.3, 550.4) 380.03 (206.2, 771.8) 2,120.65 (907.6, 3,404.2)
(Missing) 35 7 24 4
PYR_air 21.81 (6.1, 51.3) 6.42 (0.6, 8.2) 23.96 (7.7, 51.3) 108.99 (71.5, 191.4)
(Missing) 4 0 3 1
1 Median (IQR)

Primary analysis

Linear regression for each ambient exposure measurement (mix model)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *X\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the ambient exposure measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \beta_{16} * county + \beta_{17} * BMI + \beta_{18} * ses + \beta_{19} * edu + \beta_{20} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0101       0.0404
## Hannum EAA     0.2310       0.2310
## PhenoAge EAA   0.0616       0.1232
## Skin&Blood EAA 0.1150       0.1621
## GrimAge EAA    0.0011       0.0088
## DNAmTL         0.1860       0.2126
## IEAA           0.0198       0.0528
## EEAA           0.1216       0.1621

Sensitivity analysis

Linear regression for each ambient exposure measurement (simple model)

In the following section, we performed linear regression with equation \[Y = \beta_0 + \beta_1 *X + \epsilon\] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the ambient exposure measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

Likelihood ratio (LR) test (single model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \epsilon \end{aligned} \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0124       0.0440
## Hannum EAA     0.2385       0.3023
## PhenoAge EAA   0.0864       0.1728
## Skin&Blood EAA 0.2439       0.3023
## GrimAge EAA    0.0165       0.0440
## DNAmTL         0.5855       0.5855
## IEAA           0.0154       0.0440
## EEAA           0.2645       0.3023

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \epsilon \end{aligned} \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.4481       0.9041
## Hannum EAA     0.6843       0.9041
## PhenoAge EAA   0.6391       0.9041
## Skin&Blood EAA 0.9041       0.9041
## GrimAge EAA    0.1945       0.9041
## DNAmTL         0.8384       0.9041
## IEAA           0.2352       0.9041
## EEAA           0.6906       0.9041

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{bap} + \beta_2 * \text{pm25} + \beta_3 * \text{ANY} + \beta_4 * \text{BPE} + \beta_5 * \text{BaA} \\ & + \beta_6 * \text{BbF} + \beta_7 * \text{BkF} + \beta_8 * \text{CHR} + \beta_9 * \text{DBA} + \beta_{10} * \text{FLT} \\ & + \beta_{11} * \text{FLU} + \beta_{12} * \text{IPY} + \beta_{13} * \text{NAP} + \beta_{14} * \text{PHE} + \beta_{15} * \text{PYR} \\ & + \epsilon \end{aligned} \]
Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.7795       0.9656
## Hannum EAA     0.8191       0.9656
## PhenoAge EAA   0.4285       0.9656
## Skin&Blood EAA 0.8324       0.9656
## GrimAge EAA    0.0552       0.4416
## DNAmTL         0.9656       0.9656
## IEAA           0.3845       0.9656
## EEAA           0.8923       0.9656

Urinary Measurements

Summary the exposure estimates:

Characteristic Overall, N = 1121 Smokeles, N = 171 Smoky, N = 871 Wood_and_or_Plant, N = 81
Benzanthracene_Chrysene_urine 0.38 (0.3, 0.8) 0.29 (0.1, 0.6) 0.45 (0.3, 1.0) 0.36 (0.3, 0.6)
(Missing) 2 0 2 0
Naphthalene_urine 107.58 (72.1, 168.8) 96.94 (54.9, 110.9) 108.85 (73.5, 169.3) 141.97 (99.7, 174.6)
Methylnaphthalene_2_urine 26.67 (17.9, 45.0) 17.92 (8.8, 23.4) 30.18 (20.9, 46.4) 20.30 (12.2, 34.2)
(Missing) 7 0 7 0
Methylnaphthalene_1_urine 10.93 (6.6, 18.1) 5.26 (3.6, 10.5) 11.52 (7.7, 20.9) 15.06 (11.0, 26.7)
(Missing) 4 1 3 0
Acenaphthene_urine 3.14 (2.2, 7.3) 2.82 (2.2, 3.5) 3.38 (2.3, 7.9) 3.58 (2.0, 7.2)
Phenanthrene_Anthracene_urine 112.78 (42.4, 239.6) 78.75 (41.6, 135.5) 115.58 (56.8, 239.7) 109.86 (39.6, 305.8)
Fluoranthene_urine 16.53 (6.1, 23.1) 17.68 (5.4, 20.8) 15.25 (6.3, 23.2) 23.23 (22.4, 36.0)
Pyrene_urine 0.54 (0.4, 0.8) 0.41 (0.4, 0.4) 0.54 (0.4, 0.8) 0.78 (0.7, 0.9)
(Missing) 15 7 7 1
1 Median (IQR)

Primary analysis

Linear regression for each urinary exposure measurement (mix model)

In the following section, we performed linear regression with equation \[ \begin{aligned} Y = & \beta_0 + \beta_1 *X\\ & + \beta_2 * county + \beta_3 * BMI + \beta_4 * ses + \beta_5 * edu + \beta_6 * curStove + \epsilon \end{aligned} \] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the urinary exposure measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

Likelihood ratio (LR) test (mix model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \beta_{10} * county + \beta_{11} * BMI + \beta_{12} * ses + \beta_{13} * edu + \beta_{14} * curStove + \epsilon \end{aligned} \] Nested model: \[ \begin{aligned} Y = & \beta_0 \\ & + \beta_1 * county + \beta_2 * BMI + \beta_3 * ses + \beta_4 * edu + \beta_5 * curStove + \epsilon \end{aligned} \] \(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0001       0.0003
## Hannum EAA     0.0131       0.0150
## PhenoAge EAA   0.0000       0.0000
## Skin&Blood EAA 0.0009       0.0014
## GrimAge EAA    0.0004       0.0008
## DNAmTL         0.0121       0.0150
## IEAA           0.0000       0.0000
## EEAA           0.0289       0.0289

Sensitivity analysis

Linear regression for each ambient exposure measurement (simple model)

In the following section, we performed linear regression with equation \[Y = \beta_0 + \beta_1 *X + \epsilon\] where \(Y\) is one of the epigenetic age accelerations, and \(X\) is one of the urinary measurements.

The estimations of \(\beta_1\) with given \(Y\) and \(X\) are shown below, which can be interpreted as “the mean of Y changes given a one-unit increase in X while holding other variables constant”.

Likelihood ratio (LR) test (single model)

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0012       0.0048
## Hannum EAA     0.0860       0.0983
## PhenoAge EAA   0.0009       0.0048
## Skin&Blood EAA 0.0075       0.0150
## GrimAge EAA    0.0145       0.0232
## DNAmTL         0.0342       0.0456
## IEAA           0.0029       0.0077
## EEAA           0.1986       0.1986

Likelihood ratio (LR) test (single model) with subjects using only smoky or smokeless coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.0318       0.0636
## Hannum EAA     0.1305       0.1959
## PhenoAge EAA   0.0049       0.0196
## Skin&Blood EAA 0.0009       0.0072
## GrimAge EAA    0.4144       0.4144
## DNAmTL         0.1663       0.1959
## IEAA           0.0170       0.0453
## EEAA           0.1714       0.1959

Likelihood ratio (LR) test (single model) with subjects only using smoky coal

Full model: \[ \begin{aligned} Y = & \beta_0 + \beta_1 * \text{Benzanthracene_Chrysene} + \beta_2 * \text{Naphthalene} \\ & + \beta_3 * \text{2.Methylnaphthalene} + \beta_4 * \text{1.Methylnaphthalene} \\ & + \beta_5 * \text{Acenaphthene }+ \beta_6 * \text{Phenanthrene_Anthracene} \\ & + \beta_7 * \text{Phenanthrene_Anthracene} + \beta_8 * \text{Fluoranthene} + \beta_9 * \text{Pyrene} \\ & + \epsilon \end{aligned} \] Nested model: \[Y = \beta_0 + \epsilon\]

\(H_0\): The full model and the nested model fit the data equally well. Thus, you should use the nested model.
\(H_A\): The full model fits the data significantly better than the nested model. Thus, you should use the full model.

P-values results:

##                p_vals p_vals_BHadj
## Horvath EAA    0.3420       0.4325
## Hannum EAA     0.3784       0.4325
## PhenoAge EAA   0.0197       0.1076
## Skin&Blood EAA 0.1371       0.3656
## GrimAge EAA    0.4957       0.4957
## DNAmTL         0.2194       0.4325
## IEAA           0.0269       0.1076
## EEAA           0.3324       0.4325